让智能体可动态创建同伴，实现自适应团队规模。
Fluid-Agent Reinforcement Learning
- 设计可自动生成新智能体的流式环境框架
- 团队规模随环境需求动态调整，提升适应性
- 适用于需要灵活协作的现实场景如生物分裂、企业分拆
多智能体强化学习（MARL）通常研究固定数量智能体间的互动。然而现实中，智能体数量往往不固定且未知，且智能体可自主创建新个体（如细胞分裂或公司分立）。本文提出允许智能体生成其他智能体的框架，称为流式智能体环境。我们引入博弈论解概念以应对此类动态结构，并在多种基准任务中评估了多个MARL算法的表现，包括可动态产生智能体的猎物-捕食者和基于层级觅食任务。此外，我们引入了一个新环境，展示流式机制如何催生传统固定种群设置下无法出现的新策略。实验表明，该框架能生成根据环境需求自动调节规模的智能体团队。
原文摘要 · Abstract (English)
The primary focus of multi-agent reinforcement learning (MARL) has been to study interactions among a fixed number of agents embedded in an environment. However, in the real world, the number of agents is neither fixed nor known a priori. Moreover, an agent can decide to create other agents (for example, a cell may divide, or a company may spin off a division). In this paper, we propose a framework that allows agents to create other agents; we call this a fluid-agent environment. We present game-theoretic solution concepts for fluid-agent games and empirically evaluate the performance of several MARL algorithms within this framework. Our experiments include fluid variants of established benchmarks such as Predator-Prey and Level-Based Foraging, where agents can dynamically spawn, as well as a new environment we introduce that highlights how fluidity can unlock novel solution strategies beyond those observed in fixed-population settings. We demonstrate that this framework yields agent teams that adjust their size dynamically to match environmental demands.
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